Consensus clustering and functional interpretation of gene expression data

Abstract

Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene expression profiles to clusters. Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene expression analysis. Here we introduce Consensus Clustering which provides such an advantage. When coupled with a statistically based gene functional analysis, our method allowed the identification of novel Nuclear Factor-kB and Unfolded Protein Response regulated genes in certain B-cell lymphomas.